A Systematic Review of Experimental Studies on Data Glyphs

We systematically reviewed 64 user-study papers on data glyphs to help researchers and practitioners gain an informed understanding of tradeoffs in the glyph design space. The glyphs we consider are individual representations of multi-dimensional data points, often meant to be shown in small-multiple settings. Over the past 60 years many different glyph designs were proposed and many of these designs have been subjected to perceptual or comparative evaluations. Yet, a systematic overview of the types of glyphs and design variations tested, the tasks under which they were analyzed, or even the study goals and results does not yet exist. In this paper we provide such an overview by systematically sampling and tabulating the literature on data glyph studies, listing their designs, questions, data, and tasks. In addition we present a concise overview of the types of glyphs and their design characteristics analyzed by researchers in the past, and a synthesis of the study results. Based on our meta analysis of all results we further contribute a set of design implications and a discussion on open research directions.

1 Introduction

Representing multi-dimensional data is a common task in data visualization and, thus, a multitude of techniques has been developed [1]. Data glyphs are one such technique, in which single data points are encoded individually by assigning their dimensions to one or more marks and their visual variables. Data glyphs have a long history, going back to the 1950s, with metroglyphs being one of the first designs using line length to encode data [2]. A somewhat infamous, and thus well researched, example of data glyphs are Chernoff faces [3] which encode data values in 2D facial features such as the length of the nose or the orientation of eyebrows. Star Glyphs are another type of glyph-based encoding that has received research attention [4] and has been used in various applications [5], [6], [7].